Are you wondering what questions you should ask at the end of a data science interview? Well then you are in the right place! In this article we go over some of the best questions you can ask at the end of a data science interview. These questions will help you evaluate whether the role is a good fit for you and also set you up to succeed in successive interviews.
This article starts out by reviewing the main topics you should dig into in order to get a better understanding of the role. After that, we provide examples of questions that you should ask to learn more about each of these topics.
Topics to cover with your questions
Before we discuss specific questions that you should ask at the end of a data science interview, we will first discuss some of the main topics that you should probe into.
- Ideal candidate profile. What does the ideal candidate for this role look like? What technical skills is the company looking for? What characteristics will be required for success?
- Responsibilities of the role. What are the main responsibilities of the role? How much time will be spent in meetings? How much time is spent on long term projects vs ad hoc requests?
- Technology & tooling. What kind of tools are used in this role? Are open source tools more common or are there a lot of home grown tools?
- Growth & performance. What does career growth look like for someone who is in this position? How is performance evaluated at the company?
- Team structure and processes. Who is on the data science team? What rituals are followed in the data science team? How is the data science team positioned within the company?
- Company culture. What does the company value? How is the company looking to grow in the next few years?
Questions to ask at the end of a data science interview
Ideal candidate profile
One of the first things you should try to understand when interviewing for a role is the ideal candidate profile that the company is looking for. Having a better understanding of the ideal candidate profile will help you in many ways.
First, it will help you to determine whether you are a good fit for what the company is looking for. This will help you to determine whether you will be able to succeed in the role. Second, it will help you determine what skills and proficiencies you should emphasize in successive interviews. It is generally best to ask these questions early in the interview process so that you can better prepare for successive interviews.
Here are some examples of questions you can ask to determine what type of candidate the company is looking for.
- What does success look like in this role?
- What differentiates people who have performed well in this role from others?
- What are the most important characteristics are needed to succeed in this role?
- Are there any skill gaps on the team you are looking to fill?
Responsibilities of the role
The next topic you should ask questions around is the responsibilities of the role you are interviewing for. When you ask these questions, your main goal should be to determine how your time would be spent if you were working in this role.
These types of questions are particularly important for data scientists because the role of a data scientist is very different at different companies. At some companies, data scientists are more like analysts that primarily work on answering ad hoc requests for their stakeholders. At other companies, data scientists focus on building and productionalizing machine learning models.
- What would a typical day look like in this role?
- How much time is spent on ad hoc work vs long term projects?
- Is there a lot of time spent on maintenance work to keep previous projects up and running?
- How much time should I expect to spend in meetings in this role?
- What is the average length of a data science project at this company?
- Can you provide some examples of projects the team is working on?
- Does the team focus on analyses that are used to make decisions or data products that are put into production?
- What other data roles exist at the company? How do those roles compare to the data scientist role?
- What would a person in this role be expected to accomplish in the first 6 months?
Technology & tooling
The next topic you should ask questions about is the tools and technologies that are used by data scientists at that company. Here are some questions to ask related to tooling and technologies.
- What programming languages and tools are used by data scientists at the company?
- Are open source tools popular at the company? What about commercial tools? What about internally built tools?
- Are there internal tools that are built for data scientists? Who builds those tools?
- Do data scientists spend a lot of time working in notebooks?
- How large is the data someone in this role would be working with?
- How do data science models get put into production? Do data scientists write production code?
- Do data scientists in this company write unit tests for their code?
- Is object oriented programming utilized in this role?
- Do you use version control systems at this company?
Growth & performance
The next topic you should ask questions about is growth and performance. These questions should relate to the growth opportunities that are available for someone in this position and also how the company evaluates whether someone is ready for these growth opportunities.
You should try to understand how performance is evaluated in the role and what performance evaluation looks like. Is your manager the only person who provides feedback in your reviews? Or are peer reviews considered?
You should also try to understand what growth and learning opportunities exist at the company. Can data scientists request to be put on projects that will grow their skills in a particular area? Are there formal training programs that are provided to data scientists?
- How is performance evaluated at this company?
- How frequently is performance evaluated at this company?
- Who gives feedback in performance reviews?
- How closely are performance reviews tied to compensation adjustments?
- What does career growth look like for someone in this position?
- What was the growth trajectory for previous people who have worked in this position?
- Are there any training programs or formal mentorship programs at the company?
- How has your role evolved since you have joined the company?
Team structure and processes
The next topic you should ask questions around is team structure and processes. You should try to understand how the team is structured and what backgrounds other team members have. What rituals and processes does the team have to ensure that it is working together well?
You should also try to understand how the team is positioned within the company. How does the data team integrate with their stakeholders? Is the data team centralized or are data professionals embedded into different business domains?
- How many data scientists are on the team?
- What is the mix of juniors and seniors on the team?
- Are there any other data professionals on the team?
- How does the data science team work with other data professionals?
- Has the team grown over the last year?
- What is the biggest challenge the team is currently facing?
- What is the biggest accomplishment the team had this year?
- Are there code reviews within the team?
- How are conflicts around project decisions resolved?
- How far out does the team plan their roadmap?
- How are projects prioritized within the team?
- How is knowledge shared across the team?
- What stakeholders does the team interact with most?
- Do data scientists work with one specific business unit or a mix of business units?
- Is data included in roadmap planning meetings with their stakeholders?
- Where do ideas for data science projects come from?
Company culture and priorities
The final topic to ask questions about is company culture. You should try to determine what values the company holds and what effect these values have on the working environment. You should also try to understand what areas of work the company prioritizes. This can help you evaluate whether the position you are interviewing for is situated in a growing area that the company values.
- Does the company prioritize learning and development?
- How transparent is the company with important information?
- What kind of approvals are required to get the company to adopt new tools?
- Does the company offer flexibility with remote work?
- Does the company offer flexibility in working hours?
- How much vacation does the average worker take?
- What kinds of events and traditions does the company have?
- How is information shared across different departments?
- What are the companies top priorities for the upcoming years?
- What areas is the company trying to grow in during the upcoming years?
